Data Scientist II, AI Analytics

EchoStar•Denver, CO

7d•Onsite

About The Position

As a Data Scientist in AI Analytics, you will design sophisticated analytical frameworks and cutting-edge generative solutions that optimize the customer journey and streamline enterprise operations. By bridging the gap between complex data capabilities and tangible business value, you identify transformative opportunities for innovation across the organization. This role requires working at the intersection of engineering and strategy to translate high-dimensional insights into actionable roadmaps while providing technical mentorship to peers.

Requirements

Minimum Education: Bachelor’s Degree in Statistics, Machine Learning, Computer Science, Engineering, Mathematics, Physics, or a related quantitative field
Minimum Experience: 3+ years of experience in data science and applied machine learning
Python, Scala, and SQL.
Machine learning libraries (Scikit-learn, TensorFlow, or PyTorch)
Cloud-based AI platforms (AWS, Databricks, or Snowflake)
Unstructured data processing for GenAI applications
Critical experiences in applied machine learning, including a track record of delivering high-impact classification, regression, and time-series forecasting solutions
Advanced AI Application skills, specifically regarding prompt engineering, model evaluation, and the deployment of Large Language Models within production environments
Technical expertise in Python, Scala, and SQL alongside a deep understanding of modern ML libraries such as TensorFlow, PyTorch, or Scikit-learn
Hands-on experience with cloud-native data platforms like AWS, Databricks, or Snowflake to manage large-scale data engineering and processing
Strong collaboration and data storytelling skills that allow for the communication of high-dimensional insights to diverse, non-technical audiences
Analytical mastery in designing and interpreting A/B tests and controlled experiments to validate model performance and business hypotheses
Familiarity with MLOps practices for monitoring and maintaining GenAI systems in production

Responsibilities

Partner with stakeholders to identify and execute high-impact AI opportunities that directly drive measurable business outcomes
Deliver production-grade machine learning models for customer segmentation, churn prediction, and lifetime value estimation to enhance user engagement
Design and deploy scalable GenAI and LLM solutions that automate complex enterprise workflows, such as transcript analysis and compliance validation
Build cloud-based data science pipelines on enterprise platforms to ensure the reliability and scalability of analytical products
Translate complex findings into clear narratives and interactive dashboards that guide executive decision-making
Foster technical growth across the team by mentoring peers in modern MLOps practices and data storytelling techniques